Using person-specific networks in psychotherapy: challenges, limitations, and how we could use them anyway

To Use or Not to Use: Exploring Therapists' Experiences with Pre-Treatment EMA-Based Personalized Feedback in the TheraNet Project

BACKGROUND

Using idiographic network models in psychotherapy has been a growing area of interest. However, little is known about the perceived clinical utility of network models. The present study aims to explore therapists' experiences with network model-based feedback within the context of the TheraNet Project.

METHODS

In total, 18 therapists who had received network-based feedback for at least 1 patient at least 2 months prior were invited to retrospective focus groups. The focus group questions related to how participation in the study influenced the therapeutic relationship, how the networks were used, and what might improve their clinical utility. The transcribed focus groups were analyzed descriptively using qualitative content analysis.

RESULTS

Most therapists mentioned using the feedback to support their existingtheir case concept, while fewer therapists discussed the feedback directly with the patients. Several barriers to using the feedback were discussed, as well as various suggestions for how to make it more clinically useful. Many therapists reported skepticism with regards to research in the outpatient training center in general, though they were also all pleasantly surprised by being involved, having their opinions heard, and showing a readiness to adapt research to their needs/abilities.

CONCLUSIONS

This study highlights the gap between researchers' and therapists' perceptions about what useful feedback should look like. The TheraNet therapists' interest in adapting the feedback and building more informative feedback systems signals a general openness to the implementation of clinically relevant research. We provide suggestions for future implementations of network-based feedback systems in the outpatient clinical training center setting.

Which symptom to address in psychological treatment for cancer survivors when fear of cancer recurrence, depressive symptoms, and cancer-related fatigue co-occur? Exploring the level of agreement between three systematic approaches to select the focus of treatment

PURPOSE

To investigate the extent to which three systematic approaches for prioritizing symptoms lead to similar treatment advices in cancer survivors with co-occurring fear of cancer recurrence, depressive symptoms, and/or cancer-related fatigue.

METHODS

Psychological treatment advices were was based on three approaches: patient preference, symptom severity, and temporal precedence of symptoms based on ecological momentary assessments. The level of agreement was calculated according to the Kappa statistic.

RESULTS

Overall, we found limited agreement between the three approaches. Pairwise comparison showed moderate agreement between patient preference and symptom severity. Most patients preferred treatment for fatigue. Treatment for fear of cancer recurrence was mostly indicated when based on symptom severity. Agreement between temporal precedence and the other approaches was slight. A clear treatment advice based on temporal precedence was possible in 57% of cases. In cases where it was possible, all symptoms were about equally likely to be indicated.

CONCLUSIONS

The three approaches lead to different treatment advices. Future research should determine how the approaches are related to treatment outcome. We propose to discuss the results of each approach in a shared decision-making process to make a well-informed and personalized decision with regard to which symptom to target in psychological treatment.

IMPLICATIONS FOR CANCER SURVIVORS

This study contributes to the development of systematic approaches for selecting the focus of psychological treatment in cancer survivors with co-occurring symptoms by providing and comparing three different systematic approaches for prioritizing symptoms.

Exploring the complex interrelation between depressive symptoms, risk, and protective factors: A comprehensive network approach

BACKGROUND

Depressive symptoms seem to be interrelated in a complex and self-reinforcing way. To gain a better understanding of this complexity, the inclusion of theoretically relevant constructs (such as risk and protective factors) offers a comprehensive view into the complex mechanisms underlying depression.

METHODS

Cross-sectional data from individuals diagnosed with a major depressive disorder (N = 986) and healthy controls (N = 1049) were analyzed. Participants self-reported their depressive symptoms, as well as several risk factors and protective factors. Regularized partial correlation networks were estimated for each group and compared using a network comparison test.

RESULTS

Symptoms of depression were more strongly connected in the network of depressed patients than in healthy controls. Among the risk factors, perceived stress, the experience of negative life events, emotional neglect, and emotional abuse were the most centrally embedded in both networks. However, the centrality of risk factors did not significantly differ between the two groups. Among the protective factors, social support, personal competence, and acceptance were the most central in both networks, where the latter was significantly more strongly associated with the symptom of self-hate in depressed patients.

CONCLUSION

The network analysis revealed that key symptoms of depression were more strongly connected for depressed patients than for healthy controls, and that risk and protective factors play an important role, particularly perceived stress in both groups and an accepting attitude for depressed patients. However, the purpose of this study is hypothesis generating and assisting in the potential selection of non-symptom nodes for future research.

Relating stability of individual dynamical networks to change in psychopathology

One hypothesis flowing from the network theory of psychopathology is that symptom network structure is associated with psychopathology severity and in turn, one may expect that individual network structure changes with the level of psychopathology severity. However, this expectation has rarely been addressed directly. This study aims to examine (1) the stability of individual contemporaneous symptom networks over a one-year period and (2) whether network stability is associated with a change in psychopathology. We used daily diary data of n = 66 individuals, located along the psychosis severity continuum, from two separate 90-day periods, one year apart (t = 180). Based on the newly developed Individual Network Invariance Test (INIT) to assess symptom-network stability, participants were divided into two groups with stable and unstable networks and we tested whether these groups differed in their absolute change in psychopathology severity. The majority of the sample (n = 51, 77.3%) showed a stable network over time while most individuals showed a decrease in psychopathological severity. We found no significant association between a change in psychopathology severity and individual network stability. Our results call for further critical evaluation of the association between networks and psychopathology to optimize the implementation of clinical applications based on current methods.

The added value of daily diary data in 1- and 3-year prediction of psychopathology and psychotic experiences in individuals at risk for psychosis

This study aimed to assess whether adding information on psychological experiences derived from a daily diary to baseline cross-sectional data could improve short- (1-year) and long-term (3-years) prediction of psychopathology and positive psychotic experiences (PEs). We used 90-day daily diary data from 96 individuals in early subclinical risk stages for psychosis. Stepwise linear regression models were built for psychopathology and PEs at 1- and 3-years follow-up, adding: (1) baseline questionnaires, (2) the mean and variance of daily psychological experiences, and (3) individual symptom network density. We assessed whether similar results could be achieved with a subset of the data (7-14- and 30-days). The mean and variance of the diary improved model prediction of short- and long-term psychopathology and PEs, compared to prediction based on baseline questionnaires solely. Similar results were achieved with 7-14- and 30-day subsets. Symptom network density did not improve model prediction except for short-term prediction of PEs. Simple metrics, i.e., the mean and variance from 7 to 14 days of daily psychological experiences assessments, can improve short- and long-term prediction of both psychopathology and PEs in individuals in early subclinical stages for psychosis. Diary data could be a valuable addition to clinical risk prediction models for psychopathology development.

Anxiety and Depression Symptoms in Spanish Children and Adolescents: An Exploration of Comorbidity from the Network Perspective

The combination of depression and anxiety is among the most prevalent comorbidities of disorders leading to substantial functional impairment in children and adolescents. The network perspective offers a new paradigm for understanding and measuring psychological constructs and their comorbidity. The present study aims to apply network analysis to explore the comorbidity between depression and anxiety symptoms. Specifically, the study examines bridge symptoms, comorbidity, and shortest pathway networks and estimates the impact of the symptoms in the network's connectivity and structure. The findings show that "feeling lonely" and "feeling unloved" are identified as the most central bridge symptoms. The shortest path network suggests that the role of a mixed anxiety-depressive symptomatology, and specific and non-specific symptoms of clinical criteria, such as "worries," "feels depressed," "fears school," and "talks about suicide" could serve as a warning for comorbidity.

Integrating personalized experience sampling in psychotherapy: A case illustration of the Therap-i module

Background

The experience sampling methodology (ESM) is increasingly being suggested as a clinical tool in mental health care, as it offers ecologically valid, microlevel information on psychopathological processes. Patients and clinicians have recommended that applications of ESM should be personalized and integrated into the existing clinical process, but there is still much uncertainty about how implementation may look like.

Objective

To provide an example of personalized ESM assessment and feedback being integrated into psychotherapy for depression, specifically looking at the collaborative use of ESM in case conceptualization.

Methods

George, a 27-year-old man diagnosed with depression, and his therapist participated in the Therap-i randomized controlled trial, which investigates the efficacy of a personalized ESM module added to psychotherapy. Together, they created a personalized ESM questionnaire, aiming to capture their hypotheses and questions regarding George's case conceptualization. George then filled out his ESM questionnaire five times per day, for 8 weeks. During this period, ESM data were discussed and interpreted by George, his therapist, and a researcher, in three feedback sessions. In these sessions, data were visualized in a flexible feedback interface that allowed for collaborative exploration of George's data. Both patient and therapist evaluated the module through questionnaires and George also participated in a semi-structured evaluation interview.

Results

George's ESM questionnaire included personalized items on the topics of self-esteem and open versus withdrawn behavior. He completed 241 (89.3%) assessments. Discussions during the feedback sessions focused on two core themes: First, George's low energy level, which was further explored with regard to his sleep, medication, and activity patterns. Second, his low sense of self-esteem, which led to an in-depth exploration of his thinking patterns and social interactions. The ESM module was seen as useful and insightful by both George and therapist.

Conclusions

This case shows how ESM and ESM-based feedback can stimulate the collaborative exploration of the patient's complaints, and how it can provide useful insights for treatment. We discuss how our personalized ESM module relates to current clinical principles and practices, and make suggestions for further implementation.

Perceived Causal Problem Networks: Reliability, Central Problems, and Clinical Utility for Depression

Personalized case conceptualization is often regarded as a prerequisite for treatment success in psychotherapy for patients with comorbidity. This article presents Perceived Causal Networks, a novel method in which patients rate perceived causal relations among behavioral and emotional problems. First, 231 respondents screening positive for depression completed an online Perceived Causal Networks questionnaire. Median completion time (including repeat items to assess immediate test-retest reliability) was 22.7 minutes, and centrality measures showed excellent immediate test-retest reliability. Networks were highly idiosyncratic, but worrying and ruminating were the most central items for a third of respondents. Second, 50 psychotherapists rated the clinical utility of Perceived Causal Networks visualizations. Ninety-six percent rated the networks as clinically useful, and the information in the individual visualizations was judged to contain 47% of the information typically collected during a psychotherapy assessment phase. Future studies should individualize networks further and evaluate the validity of perceived causal relations.

Personalised app-based relapse prevention of depressive and anxiety disorders in remitted adolescents and young adults: a protocol of the StayFine RCT

INTRODUCTION

Youth in remission of depression or anxiety have high risks of relapse. Relapse prevention interventions may prevent chronicity. Aim of the study is therefore to (1) examine efficacy of the personalised StayFine app for remitted youth and (2) identify high-risk groups for relapse and resilience.

METHOD AND ANALYSIS

In this Dutch single-blind parallel-group randomised controlled trial, efficacy of app-based monitoring combined with guided app-based personalised StayFine intervention modules is assessed compared with monitoring only. In both conditions, care as usual is allowed. StayFine modules plus monitoring is hypothesised to be superior to monitoring only in preventing relapse over 36 months. Participants (N=254) are 13-21 years and in remission of depression or anxiety for >2 months. Randomisation (1:1) is stratified by previous treatment (no treatment vs treatment) and previous episodes (1, 2 or >3 episodes). Assessments include diagnostic interviews, online questionnaires and monitoring (ecological momentary assessment with optional wearable) after 0, 4, 12, 24 and 36 months. The StayFine modules are guided by certified experts by experience and based on preventive cognitive therapy and ingredients of cognitive behavioural therapy. Personalisation is based on shared decision-making informed by baseline assessments and individual symptom networks. Time to relapse (primary outcome) is assessed by the Kiddie Schedule for Affective Disorders and Schizophrenia-lifetime version diagnostic interview. Intention-to-treat survival analyses will be used to examine the data. Secondary outcomes are symptoms of depression and anxiety, number and duration of relapses, global functioning, and quality of life. Mediators and moderators will be explored. Exploratory endpoints are monitoring and wearable outcomes.

ETHICS, FUNDING AND DISSEMINATION

The study was approved by METC Utrecht and is funded by the Netherlands Organisation for Health Research and Development (636310007). Results will be submitted to peer-reviewed scientific journals and presented at (inter)national conferences.

TRIAL REGISTRATION NUMBER

NCT05551468; NL8237.

Integrating clinician and patient case conceptualization with momentary assessment data to construct idiographic networks: Moving toward personalized treatment for eating disorders

Eating disorders are serious psychiatric illnesses with treatments ineffective for about 50% of individuals due to high heterogeneity of symptom presentation even within the same diagnoses, a lack of personalized treatments to address this heterogeneity, and the fact that clinicians are left to rely upon their own judgment to decide how to personalize treatment. Idiographic (personalized) networks can be estimated from ecological momentary assessment data, and have been used to investigate central symptoms, which are theorized to be fruitful treatment targets. However, both efficacy of treatment target selection and implementation with 'real world' clinicians could be maximized if clinician input is integrated into such networks. An emerging line of research is therefore proposing to integrate case conceptualizations and statistical routines, tying together the benefits from clinical expertise as well as patient experience and idiographic networks. The current pilot compares personalized treatment implications from different approaches to constructing idiographic networks. For two patients with a diagnosis of anorexia nervosa, we compared idiographic networks 1) based on the case conceptualization from clinician and patient, 2) estimated from patient EMA data (the current default in the literature), and 3) based on a combination of case conceptualization and patient EMA data networks, drawing on informative priors in Bayesian inference. Centrality-based treatment recommendations differed to varying extent between these approaches for patients. We discuss implications from these findings, as well as how these models may inform clinical practice by pairing evidence-based treatments with identified treatment targets.

Practitioner and researcher perspectives on the utility of ecological momentary assessment in mental health care: A survey study

BACKGROUND

Ecological momentary assessment (EMA) is a scientific self-monitoring method to capture individuals' daily life experiences. Early on, EMA has been suggested to have the potential to improve mental health care. However, it remains unclear if and how EMA should be implemented. This requires an in-depth investigation of how practitioners and researchers view the implementation of EMA.

OBJECTIVE

Explore the perspectives of mental health practitioners and EMA researchers on the utility of EMA for mental health care.

METHODS

Practitioners (n = 89; psychiatrists, psychologists, psychiatric nurses) and EMA researchers (n = 62) completed a survey about EMA in clinical practice. This survey addressed EMA goals for practitioner and patient, requirements regarding clinical use of EMA, and (dis)advantages of EMA compared to treatment-as-usual. t-Tests were used to determine agreement with each statement and whether practitioners' and researchers' views differed significantly. Linear regression was used to explore predictors of goals and preferences (e.g., EMA experience).

RESULTS

Practitioners and researchers considered EMA to be a useful clinical tool for diverse stages of care. They indicated EMA to be most useful for gaining insight into the context specificity of symptoms (55.0 %), whereas receiving alerts when symptoms increase was rated the least useful (11.3 %, alerts is in 95 % of bootstrap iterations between rank 8 and 10). Compared to treatment-as-usual, EMA was considered easier to use (M = 4.87, t = 5.30, p < .001) and interpret (M = 4.52, t = 3.61, p < .001), but also more burdensome for the patient (M = 4.48, t = 3.17, p < .001). Although participants preferred personalization of the EMA diary, they also suggested that EMA should cost practitioners and patients limited time. The preference for creating personalized EMA was related to the level of experience with EMA. Finally, they highlighted the need for practitioner training and patient full-time access to the EMA feedback.

CONCLUSIONS

This survey study demonstrated that practitioners and researchers expect EMA to have added value for mental health care. Concrete recommendations for implementation of EMA are formulated. This may inform the development of specific clinical applications and user-friendly EMA software.

Using smartphone-based ecological momentary assessment and personalized feedback for patients with chronic cancer-related fatigue: A proof-of-concept study

INTRODUCTION

Chronic cancer-related fatigue (CCRF) is a complex multidimensional problem warranting person-centered care. Providing patients and therapists personalized feedback based on network analysis applied to ecological momentary assessment (EMA) data could facilitate case conceptualization in psycho-oncological care. The aim was to explore patients' and therapists' experiences of using an EMA app and personalized feedback based on network theory to aid case conceptualization in psycho-oncological care.

METHODS

A n = 5 proof-of-concept study was implemented in routine psycho-oncological care. We purposively selected adult cancer patients suffering from severe CCRF who were on the waitlist for psycho-oncological care. During a 3-week period participants filled out the EMA app Energy InSight (fatigue, mood, activity, responding, and context) five times a day. Participants received a descriptive and network feedback report, which they reflected upon during the first therapy sessions. Thematic analysis was used to analyze user experiences.

RESULTS

Patients experienced that filling out the Energy InSight app, as well as receiving descriptive and network-based personalized feedback provided them with insight into their CCRF. Although therapists experienced the discussion of network feedback as challenging, it facilitated the case conceptualization.

DISCUSSION

Using EMA during waitlist for psychological care seemed feasible. Patients experienced beneficial effects from filling out the EMA app and talking over the personalized feedback reports, which in turn aided case conceptualization and personalized care. Based on this evaluation, an improved version of the Energy InSight app and a therapist training for providing network feedback is developed for implementation in psycho-oncological care.

A Web-Based Application for Personalized Ecological Momentary Assessment in Psychiatric Care: User-Centered Development of the PETRA Application

BACKGROUND

Smartphone self-monitoring of mood, symptoms, and contextual factors through ecological momentary assessment (EMA) provides insights into the daily lives of people undergoing psychiatric treatment. Therefore, EMA has the potential to improve their care. To integrate EMA into treatment, a clinical tool that helps clients and clinicians create personalized EMA diaries and interpret the gathered data is needed.

OBJECTIVE

This study aimed to develop a web-based application for personalized EMA in specialized psychiatric care in close collaboration with all stakeholders (ie, clients, clinicians, researchers, and software developers).

METHODS

The participants were 52 clients with mood, anxiety, and psychotic disorders and 45 clinicians (psychiatrists, psychologists, and psychiatric nurses). We engaged them in interviews, focus groups, and usability sessions to determine the requirements for an EMA web application and repeatedly obtained feedback on iteratively improved high-fidelity EMA web application prototypes. We used human-centered design principles to determine important requirements for the web application and designed high-fidelity prototypes that were continuously re-evaluated and adapted.

RESULTS

The iterative development process resulted in Personalized Treatment by Real-time Assessment (PETRA), which is a scientifically grounded web application for the integration of personalized EMA in Dutch clinical care. PETRA includes a decision aid to support clients and clinicians with constructing personalized EMA diaries, an EMA diary item repository, an SMS text message-based diary delivery system, and a feedback module for visualizing the gathered EMA data. PETRA is integrated into electronic health record systems to ensure ease of use and sustainable integration in clinical care and adheres to privacy regulations.

CONCLUSIONS

PETRA was built to fulfill the needs of clients and clinicians for a user-friendly and personalized EMA tool embedded in routine psychiatric care. PETRA is unique in this codevelopment process, its extensive but user-friendly personalization options, its integration into electronic health record systems, its transdiagnostic focus, and its strong scientific foundation in the design of EMA diaries and feedback. The clinical effectiveness of integrating personalized diaries via PETRA into care requires further research. As such, PETRA paves the way for a systematic investigation of the utility of personalized EMA for routine mental health care.

The Mereology of Depression-Networks of Depressive Symptoms during the Course of Psychotherapy

(1) Background: Research has shown that it is important to examine depressive symptoms in the light of the mereology (the ratio between one symptom and the whole disorder). The goal of this study was to examine changes in the symptom interrelations of patients undergoing cognitive behavioral therapy treatment (CBT) via network analyses. (2) Method: Outpatients with depressive symptoms (N = 401) were assessed with the Beck Depression Inventory three times (pretreatment, after 12 sessions, and post-treatment) during CBT. Gaussian graphical models were used to estimate the relationships among symptoms. (3) Results: The severity of depressive symptoms significantly decreased over the course of therapy, but connectivity in the networks significantly increased. Communities of symptoms changed during treatment. The most central and predictable symptom was worthlessness at baseline and after 12 sessions, and loss of energy and self-dislike at post-treatment. (4) Conclusion: The results indicate that the severity of depressive symptoms decreased during cognitive behavior therapy, while network connectivity increased. Furthermore, the associations among symptoms and their centrality changed during the course of therapy. Future studies may investigate individual differences and their impact on the planning of psychotherapeutic treatment.

Don't Miss the Moment: A Systematic Review of Ecological Momentary Assessment in Suicide Research

Suicide and suicide-related behaviors are prevalent yet notoriously difficult to predict. Specifically, short-term predictors and correlates of suicide risk remain largely unknown. Ecological momentary assessment (EMA) may be used to assess how suicidal thoughts and behaviors (STBs) unfold in real-world contexts. We conducted a systematic literature review of EMA studies in suicide research to assess (1) how EMA has been utilized in the study of STBs (i.e., methodology, findings), and (2) the feasibility, validity and safety of EMA in the study of STBs. We identified 45 articles, detailing 23 studies. Studies mainly focused on examining how known longitudinal predictors of suicidal ideation perform within shorter (hourly, daily) time frames. Recent studies have explored the prospects of digital phenotyping of individuals with suicidal ideation. The results indicate that suicidal ideation fluctuates substantially over time (hours, days), and that individuals with higher mean ideation also have more fluctuations. Higher suicidal ideation instability may represent a phenotypic indicator for increased suicide risk. Few studies succeeded in establishing prospective predictors of suicidal ideation beyond prior ideation itself. Some studies show negative affect, hopelessness and burdensomeness to predict increased ideation within-day, and sleep characteristics to impact next-day ideation. The feasibility of EMA is encouraging: agreement to participate in EMA research was moderate to high (median = 77%), and compliance rates similar to those in other clinical samples (median response rate = 70%). More individuals reported suicidal ideation through EMA than traditional (retrospective) self-report measures. Regarding safety, no evidence was found of systematic reactivity of mood or suicidal ideation to repeated assessments of STBs. In conclusion, suicidal ideation can fluctuate substantially over short periods of time, and EMA is a suitable method for capturing these fluctuations. Some specific predictors of subsequent ideation have been identified, but these findings warrant further replication. While repeated EMA assessments do not appear to result in systematic reactivity in STBs, participant burden and safety remains a consideration when studying high-risk populations. Considerations for designing and reporting on EMA studies in suicide research are discussed.

Psychopathological networks: Theory, methods and practice

In recent years, network approaches to psychopathology have sparked much debate and have had a significant impact on how mental disorders are perceived in the field of clinical psychology. However, there are many important challenges in moving from theory to empirical research and clinical practice and vice versa. Therefore, in this article, we bring together different points of view on psychological networks by methodologists and clinicians to give a critical overview on these challenges, and to present an agenda for addressing these challenges. In contrast to previous reviews, we especially focus on methodological issues related to temporal networks. This includes topics such as selecting and assessing the quality of the nodes in the network, distinguishing between- and within-person effects in networks, relating items that are measured at different time scales, and dealing with changes in network structures. These issues are not only important for researchers using network models on empirical data, but also for clinicians, who are increasingly likely to encounter (person-specific) networks in the consulting room.

Person-specific networks in psychopathology: Past, present, and future

In the psychological network approach, mental disorders such as major depressive disorder are conceptualized as networks. The network approach focuses on the symptom structure or the connections between symptoms instead of the severity (i.e., mean level) of a symptom. To infer a person-specific network for a patient, time-series data are needed. By far the most common model to statistically model the person-specific interactions between symptoms or momentary states has been the vector autoregressive (VAR) model. Although the VAR model helps to bring psychological network theory into clinical research and closer to clinical practice, several discrepancies arise when we map the psychological network theory onto the VAR-based network models. These challenges and possible solutions are discussed in this review.

The Mental Health Ecosystem: Extending Symptom Networks With Risk and Protective Factors

Inspired by modeling approaches from the ecosystems literature, in this paper, we expand the network approach to psychopathology with risk and protective factors to arrive at an integrated analysis of resilience. We take a complexity approach to investigate the multifactorial nature of resilience and present a system in which a network of interacting psychiatric symptoms is targeted by risk and protective factors. These risk and protective factors influence symptom development patterns and thereby increase or decrease the probability that the symptom network is pulled toward a healthy or disorder state. In this way, risk and protective factors influence the resilience of the network. We take a step forward in formalizing the proposed system by implementing it in a statistical model and translating different influences from risk and protective factors to specific targets on the node and edge parameters of the symptom network. To analyze the behavior of the system under different targets, we present two novel network resilience metrics: Expected Symptom Activity (ESA, which indicates how many symptoms are active or inactive) and Symptom Activity Stability (SAS, which indicates how stable the symptom activity patterns are). These metrics follow standard practices in the resilience literature, combined with ideas from ecology and physics, and characterize resilience in terms of the stability of the system's healthy state. By discussing the advantages and limitations of our proposed system and metrics, we provide concrete suggestions for the further development of a comprehensive modeling approach to study the complex relationship between risk and protective factors and resilience.

Personalized ESM monitoring and feedback to support psychological treatment for depression: a pragmatic randomized controlled trial (Therap-i)

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent mental disorder with large disease burden, high levels of relapse or persistence, and overall suboptimal outcomes of protocolized pharmacological and psychotherapeutic treatments. There is an urgent need to improve treatment effectiveness, possibly through systematic treatment personalization. In psychotherapeutic treatments this can be achieved by case conceptualization. To support this process, we developed the Therap-i module, which consists of personalized Experienced Sampling Methodology (ESM) and feedback. The Therap-i module is integrated into outpatient psychotherapeutic treatment as usual (TAU) for depression. The study aim is to investigate the efficacy of the Therap-i module in decreasing symptomatology in unresponsive or relapsing patients diagnosed with MDD. We hypothesize that the Therap-i module will contribute to TAU by i) decreasing depressive symptoms, and ii) improving general functioning, therapeutic working alliance, and illness perception. This paper provides details of the study rationale, aims, procedures, and a discussion on potential pitfalls and promises of the module.

METHODS

Patients diagnosed with MDD (n = 100) will enrol in a pragmatic two-armed randomized controlled trial. Randomization is stratified according to the patient's treatment resistance level assessed with the Dutch Method for quantification of Treatment Resistance in Depression (DM-TRD). All fill-out the Inventory of Depressive Symptomatology Self Report (IDS-SR), Outcome Questionnaire (OQ-45), Illness Perception Questionnaire Mental Health (IPQ-MH), and Work Alliance Inventory Self Report (WAI-SR). In the intervention arm, through close collaboration between patient, clinician, and researcher, a personalized ESM diary is developed based on the patient's case conceptualization. During the ESM monitoring period (8 weeks, 5 assessments/day), patients receive feedback three times, which is discussed among the abovementioned three parties. Both patients and clinicians will evaluate the Therap-i module.

RESULTS

Data collection is ongoing.

DISCUSSION

This is the first study in which personalized ESM and feedback is integrated in outpatient psychotherapeutic TAU for depression. The labour intensive procedure and methodological pitfalls are anticipated challenges and were taken into account when designing the study. When hypotheses are confirmed, the Therap-i module may advance treatment for depression by providing insights into personalized patterns driving or perpetuating depressive complaints.

TRIAL REGISTRATION

Trial NL7190 (NTR7381) , registered prospectively 03-08-2018.

Network dynamics of momentary affect states and future course of psychopathology in adolescents

BACKGROUND

Recent theories argue that an interplay between (i.e., network of) experiences, thoughts and affect in daily life may underlie the development of psychopathology.

OBJECTIVE

To prospectively examine whether network dynamics of everyday affect states are associated with a future course of psychopathology in adolescents at an increased risk of mental disorders.

METHODS

159 adolescents from the East-Flanders Prospective Twin Study cohort participated in the study. At baseline, their momentary affect states were assessed using the Experience Sampling Method (ESM). The course of psychopathology was operationalized as the change in the Symptom Checklist-90 sum score after 1 year. Two groups were defined: one with a stable level (n = 81) and one with an increasing level (n = 78) of SCL-symptom severity. Group-level network dynamics of momentary positive and negative affect states were compared between groups.

RESULTS

The group with increasing symptoms showed a stronger connections between negative affect states and their higher influence on positive states, as well as higher proneness to form 'vicious cycles', compared to the stable group. Based on permutation tests, these differences were not statistically significant.

CONCLUSION

Although not statistically significant, some qualitative differences were observed between the networks of the two groups. More studies are needed to determine the value of momentary affect networks for predicting the course of psychopathology.

A Narrative Review of Network Studies in Depression: What Different Methodological Approaches Tell Us About Depression

The network theory of psychopathology proposes that mental disorders arise from direct interactions between symptoms. This theory provides a promising framework to understand the development and maintenance of mental disorders such as depression. In this narrative review, we summarize the literature on network studies in the field of depression. Four methodological network approaches are distinguished: (i) studies focusing on symptoms at the macro-level vs. (ii) on momentary states at the micro-level, and (iii) studies based on cross-sectional vs. (iv) time-series (dynamic) data. Fifty-six studies were identified. We found that different methodological approaches to network theory yielded largely inconsistent findings on depression. Centrality is a notable exception: the majority of studies identified either positive affect or anhedonia as central nodes. To aid future research in this field, we outline a novel complementary network theory, the momentary affect dynamics (MAD) network theory, to understand the development of depression. Furthermore, we provide directions for future research and discuss if and how networks might be used in clinical practice. We conclude that more empirical network studies are needed to determine whether the network theory of psychopathology can indeed enhance our understanding of the underlying structure of depression and advance clinical treatment.